Automated driving management system and automated driving management method

ABSTRACT

An automated driving management system is applied to a vehicle that performs automated driving by using a perception sensor. The automated driving management system acquires first sensor perception information indicating a result of perception by the perception sensor. Reference information indicates a correspondence relationship between a vehicle position and expected sensor perception information that is the first sensor perception information expected when an automated driving condition is satisfied. Based on the reference information, the automated driving management system acquires the expected sensor perception information associated with a determination target position. The automated driving management system determines whether or not the automated driving condition is satisfied at the determination target position by comparing the first sensor perception information acquired at the determination target position with the expected sensor perception information associated with the determination target position.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2022-118084 filed on Jul. 25, 2022, the entire contents of which areincorporated by reference herein.

BACKGROUND Technical Field

The present disclosure relates to a technique for managing automateddriving of a vehicle. In particular, the present disclosure relates to atechnique for determining whether or not an automated driving conditionis satisfied.

Background Art

Patent Literature 1 discloses an autonomous traveling vehicle providedwith a plurality of sensors. The autonomous traveling vehicle evaluatesa state of dirt or failure of the sensors. When a sensor performance isdegraded due to the dirt or failure, the autonomous traveling vehicleoperates in a degenerate mode in which a speed and a steering angle arelimited.

Patent Literature 2 discloses an electronic control device installed ona vehicle. The electronic control device determines a sensor detectableregion based on detection information of a sensor installed on thevehicle. The electronic control device generates travel controlinformation of the vehicle based on information detected by the sensorand a sensor detectable area.

LIST OF RELATED ART

-   -   Patent Literature 1: International Publication No. WO2015/068249    -   Patent Literature 2: Japanese Laid-Open Patent Application No.        JP-2021-187324

SUMMARY

Automated driving of a vehicle is considered. An automated drivingcondition is a condition under which the automated driving of thevehicle is permitted, and is also referred to as an operational designdomain (ODD). An automated driving system is designed to be operatedunder a predetermined automated driving condition (ODD). Therefore, whenperforming the automated driving, it is important to determine whetheror not the automated driving condition is satisfied.

An object of the present disclosure is to provide a technique capable ofmore accurately determining whether or not an automated drivingcondition is satisfied.

A first aspect relates to an automated driving management system.

The automated driving management system is applied to a vehicle thatperforms automated driving by using a perception sensor for perceiving asurrounding situation.

The automated driving management system includes:

-   -   one or more processors configured to acquire first sensor        perception information indicating a result of perception by the        perception sensor; and    -   one or more memory devices configured to store reference        information indicating a correspondence relationship between a        vehicle position and expected sensor perception information that        is the first sensor perception information expected when an        automated driving condition is satisfied.

The one or more processors acquire, based on the reference information,the expected sensor perception information associated with adetermination target position.

The one or more processors determine whether or not the automateddriving condition is satisfied at the determination target position bycomparing the first sensor perception information acquired at thedetermination target position with the expected sensor perceptioninformation associated with the determination target position.

A second aspect relates to an automated driving management method.

The automated driving management method is applied to a vehicle thatperforms automated driving by using a perception sensor for perceiving asurrounding situation.

First sensor perception information indicates a result of perception bythe perception sensor.

Reference information indicates a correspondence relationship between avehicle position and expected sensor perception information that is thefirst sensor perception information expected when an automated drivingcondition is satisfied.

The automated driving management method includes:

-   -   acquiring, based on the reference information, the expected        sensor perception information associated with a determination        target position; and    -   determining whether or not the automated driving condition is        satisfied at the determination target position by comparing the        first sensor perception information acquired at the        determination target position with the expected sensor        perception information associated with the determination target        position.

According to the present disclosure, the reference informationindicating the correspondence relationship between the vehicle positionand the expected sensor perception information is prepared. The expectedsensor perception information is the first sensor perception information(i.e., the result of perception by the perception sensor) expected whenthe automated driving condition is satisfied. Therefore, comparing thefirst sensor perception information acquired at the determination targetposition with the expected sensor perception information associated withthe determination target position makes it possible to accuratelydetermine whether or not the automated driving condition is satisfied atthe determination target position.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram for explaining an overview of a vehicleand a vehicle control system according to an embodiment of the presentdisclosure;

FIG. 2 is a conceptual diagram for explaining an overview of an ODDsuitability determination process according to an embodiment of thepresent disclosure;

FIG. 3 is a diagram for explaining an example of an ODD suitabilitydetermination process according to an embodiment of the presentdisclosure;

FIG. 4 is a conceptual diagram for explaining a management serveraccording to an embodiment of the present disclosure;

FIG. 5 is a block diagram showing a configuration example of a vehiclecontrol system according to an embodiment of the present disclosure;

FIG. 6 is a block diagram showing an example of driving environmentinformation according to an embodiment of the present disclosure;

FIG. 7 is a conceptual diagram for explaining a localization processaccording to an embodiment of the present disclosure;

FIG. 8 is a block diagram showing a configuration example of anautomated driving management system according to an embodiment of thepresent disclosure;

FIG. 9 is a flowchart showing an example of processing by an automateddriving management system according to an embodiment of the presentdisclosure;

FIG. 10 is a conceptual diagram for explaining an example of point cloudinformation according to an embodiment of the present disclosure;

FIG. 11 is a conceptual diagram for explaining an example of referenceinformation according to an embodiment of the present disclosure;

FIG. 12 is a conceptual diagram for explaining an example of an ODDsuitability determination process according to an embodiment of thepresent disclosure;

FIG. 13 is a conceptual diagram for explaining another example of an ODDsuitability determination process according to an embodiment of thepresent disclosure;

FIG. 14 is a conceptual diagram for explaining still another example ofan ODD suitability determination process according to an embodiment ofthe present disclosure;

FIG. 15 is a conceptual diagram for explaining still another example ofan ODD suitability determination process according to an embodiment ofthe present disclosure; and

FIG. 16 is a conceptual diagram for explaining still another example ofan ODD suitability determination process according to an embodiment ofthe present disclosure.

EMBODIMENTS

Embodiments of the present disclosure will be described with referenceto the accompanying drawings.

1. Overview 1-1. Vehicle Control System

FIG. 1 is a conceptual diagram for explaining an overview of a vehicle 1and a vehicle control system 10 according to the present embodiment. Thevehicle control system 10 controls the vehicle 1. Typically, the vehiclecontrol system 10 is installed on the vehicle 1. Alternatively, at leasta part of the vehicle control system 10 may be included in a remotesystem outside the vehicle 1 to remotely control the vehicle 1.

The vehicle 1 is capable of automated driving, and the vehicle controlsystem is configured to control the automated driving of the vehicle 1.The automated driving supposed here is one where a driver may notnecessarily 100% concentrate on the driving (e.g., so-called Level 3 orhigher level automated driving). The automated driving level may beLevel 4 or higher that does not need a driver.

In the automated driving of the vehicle 1, a perception sensor 30mounted on the vehicle 1 is used. The perception sensor 30 is a sensorfor perceiving a situation around the vehicle 1. Examples of theperception sensor 30 include a laser imaging detection and ranging(LIDAR), a camera, a radar, and the like. The LIDAR emits beams anddetects a reflected beam reflected at a reflection point to measure arelative position of the reflection point.

The vehicle control system 10 uses the perception sensor 30 to perceivea situation around the vehicle 1. For example, the vehicle controlsystem 10 uses the perception sensor 30 to perceive a stationary objectand a moving object around the vehicle 1. Examples of the stationaryobject include a road surface 2, a road structure 3 (e.g., a wall, aguardrail, a curb), a white line, and the like. Examples of the movingobject include a surrounding vehicle 4, a pedestrian 5, and the like.Then, the vehicle control system 10 executes automated driving controlregarding the vehicle 1 based on a result of the perception processingusing the perception sensor 30.

1-2. Automated Driving Condition

An automated driving condition is a condition under which the automateddriving of the vehicle 1 is permitted. The automated driving conditionis also referred to as an operational design domain (ODD) or anoperation design domain. Generally, the automated driving condition isdefined by a maximum vehicle speed, a traveling area, a weathercondition, a sunshine condition, and the like. For example, in rainyweather, accuracy of the perception processing using the perceptionsensor 30 may decrease, and thus accuracy of the automated drivingcontrol may decrease. Therefore, conventionally, “a rainfall amount perunit time is less than a predetermined value (for example, 5 mm/h)” hasbeen used as one of the automated driving condition related to theweather.

The vehicle control system 10 is designed to perform the automateddriving under a predetermined automated driving condition (ODD).Therefore, when performing the automated driving, it is important todetermine whether or not the automated driving condition is satisfied. Aprocess of determining whether or not the automated driving condition issatisfied is hereinafter referred to as an “ODD suitabilitydetermination process.” The inventor of the present application hasrecognized the following problem regarding the ODD suitabilitydetermination process.

As an example, the above-mentioned automated driving condition relatedto the weather, “a rainfall amount per unit time is less than apredetermined value” is considered. The rainfall amount varies widelyeven in a relatively narrow area, and local torrential rain hasincreased in recent years. Therefore, it is not easy to accuratelypinpoint the rainfall amount at a current position of the vehicle 1. Inorder to increase accuracy of measurement of the local rainfall amount,a large-scale infrastructure such as deployment of a large number ofrainfall amount sensors is required. This is not desirable from aviewpoint of costs. In addition, when the sun shines under a wet roadsurface condition after rain, reflection of light from the wet roadsurface increases. In this case, accuracy of perception of the roadsurface and a fallen object by the perception sensor 30 may decrease.That is, even when it is not raining, there is a possibility that anenvironment around the vehicle 1 is not desirable for the automateddriving. Therefore, performing the ODD suitability determination processbased on a simple comparison between the rainfall amount and a thresholdvalue is not necessarily appropriate from a viewpoint of the accuracy ofthe automated driving control.

In a case of a weather that is difficult to measure such as fog,difficulty of the ODD suitability determination process furtherincreases.

In addition, not only the natural environment such as the weather butalso aging or performance deterioration of the perception sensor 30itself affects the accuracy of the automated driving control. It isnecessary to perform the ODD suitability determination process inconsideration of various factors that affect the accuracy of theautomated driving control.

In view of the above, the present disclosure proposes a new techniquecapable of improving the accuracy of the ODD suitability determinationprocess.

1-3. New ODD Suitability Determination Process

First, a technical concept of the new ODD suitability determinationprocess according to the present embodiment will be described.

As described above, it is not necessarily appropriate to perform the ODDsuitability determination process using a parameter such as the rainfallamount that specifically defines the weather itself. A human driver doesnot decide whether it is easy or difficult to drive by looking at thespecific parameter such as the rainfall amount. The human driver decideswhether it is easy or difficult to drive based on information perceivedby the human driver's own vision. For example, when the sun shines undera wet road surface condition after rain, reflection of light from thewet road surface is so bright that the road surface cannot be seen asusual, and thus the human driver decides that it is difficult to drive.That is, the human driver decides that it is difficult to drive, whenthe information perceived by the human driver's own vision is differentfrom usual one.

The ODD suitability determination process according to the presentembodiment also is performed in the same manner as a sense of the humandriver. An “eye” for the vehicle control system 10 that performs theautomated driving control is the perception sensor 30. Therefore,according to the present embodiment, the ODD suitability determinationprocess is performed based on the result of perception by the perceptionsensor 30. That is, the ODD suitability determination process isperformed based on whether “appearance” viewed from the perceptionsensor 30 of the vehicle 1 is as usual or not.

FIG. 2 is a conceptual diagram for explaining an overview of the ODDsuitability determination process according to the present embodiment.“First sensor perception information SEN1” indicates the result ofperception by the perception sensor 30 mounted on the vehicle 1. Thatis, the first sensor perception information SEN1 corresponds to the“appearance” viewed from the perception sensor 30. For example, when theperception sensor 30 includes the LIDAR, the first sensor perceptioninformation SEN1 includes information of a point cloud (beam reflectionpoints) measured by the LIDAR. For example, the first sensor perceptioninformation SEN1 includes the number of beam reflection points on theroad surface 2 measured during one frame.

Typically, the first sensor perception information SEN1 includesinformation on a stationary object (e.g., the road surface 2, the roadstructure 3) perceived by the perception sensor 30. On the other hand,information on a moving object (e.g., the surrounding vehicle 4, thepedestrian 5) perceived by the perception sensor 30 may not necessarilybe included in the first sensor perception information SEN1. Forconvenience sake, the information on the moving object perceived by theperception sensor 30 is hereinafter referred to as “second sensorperception information.” The second sensor perception information isnecessary for the automated driving control by the vehicle controlsystem 10, but may not necessarily be included in the first sensorperception information SEN1.

“Expected sensor perception information ESEN” is the first sensorperception information SEN1 expected when the automated drivingcondition is satisfied. The automated driving condition is determined inadvance in consideration of various factors that affect the accuracy ofthe automated driving control. The expected sensor perceptioninformation ESEN corresponds to “appearance” viewed from the perceptionsensor 30 in the case where the automated driving is permissible.

“Reference information REF” indicates a correspondence relationshipbetween a vehicle position PV and the expected sensor perceptioninformation ESEN. That is, the reference information REF indicates theexpected sensor perception information ESEN as a function of the vehicleposition PV. It can also be said that the reference information REFindicates the expected sensor perception information ESEN when thevehicle 1 is present at the vehicle position PV.

The vehicle position PV may be set along a general vehicle traveltrajectory in a road. The vehicle position PV may be assumed to belocated at a lane center. The vehicle position PV may be a conceptincluding both a position and a direction of the vehicle 1. Thedirection of the vehicle 1 may be assumed to be parallel to an extendingdirection of a lane (white line).

The expected sensor perception information ESEN and the referenceinformation REF are generated and updated based on information acquiredwhen the automated driving condition is satisfied. For example, theexpected sensor perception information ESEN and the referenceinformation REF are generated and updated based on past automateddriving records of one or more vehicles 1. In this case, it can be saidthat the expected sensor perception information ESEN and the referenceinformation REF represent “past successful experiences.” As anotherexample, the reference information REF may be generated and updatedthrough a simulation based on configuration information of an automateddriving area and design information of the perception sensor 30.

It can be said that the reference information REF that represents theexpected sensor perception information ESEN as a function of the vehicleposition PV is a kind of map information. However, it should be notedthat the reference information REF is a concept that is totallydifferent from general map information. The general map informationindicates an arrangement of objects in an absolute coordinate system.That is, the general map information indicates a correspondencerelationship between an absolute position and an object present at theabsolute position. On the other hand, the reference information REFindicates a correspondence relationship between the vehicle position PVand the first sensor perception information SEN1 (i.e., the result ofperception) as viewed from the vehicle position PV when the automateddriving condition is satisfied. The reference information REF does notindicate an object present at the vehicle position PV.

An automated driving management system 100 is applied to the vehicle 1and manages the automated driving of the vehicle 1. The automateddriving management system 100 holds the above-described referenceinformation REF, and performs the ODD suitability determination processregarding the vehicle 1 based on the reference information REF. Inparticular, the automated driving management system 100 determineswhether or not the automated driving condition is satisfied for thevehicle 1 present at a determination target position PT. Typically, thedetermination target position PT is the current position of the vehicle1. As another example, the determination target position PT may be apast position of the vehicle 1.

More specifically, the automated driving management system 100 acquiresthe first sensor perception information SEN1 acquired by the vehicle 1(i.e., the vehicle control system 10) at the determination targetposition PT. In addition, the automated driving management system 100acquires, based on the reference information REF, the expected sensorperception information ESEN associated with the determination targetposition PT. The expected sensor perception information ESEN associatedwith the determination target position PT is the first sensor perceptioninformation SEN1 expected at the determination target position PT whenthe automated driving condition is satisfied. Therefore, the automateddriving management system 100 is able to determine whether or not theautomated driving condition is satisfied at the determination targetposition PT by comparing the first sensor perception information SEN1acquired at the determination target position PT with the expectedsensor perception information ESEN associated with the determinationtarget position PT. When the first sensor perception information SEN1acquired at the determination target position PT is significantlydifferent from the expected sensor perception information ESENassociated with the determination target position PT, the automateddriving management system 100 determines that the automated drivingcondition is not satisfied at the determination target position PT.

FIG. 3 is a diagram for explaining an example of the ODD suitabilitydetermination process according to the present embodiment. In FIG. 3 , ahorizontal axis represents the vehicle position PV and a vertical axisrepresents a parameter X. The parameter X is a parameter indicating theresult of perception by the perception sensor 30 and is included in thefirst sensor perception information SEN1. For example, the parameter Xis the number of beam reflection points on the road surface 2 measuredby the LIDAR.

The expected sensor perception information ESEN includes an expectedvalue Xe of the parameter X expected when the automated drivingcondition is satisfied. For example, the expected value Xe is an averagevalue of a large number of parameters X acquired when the automateddriving condition is satisfied. The reference information REF indicatesa correspondence relationship between the expected value Xe of theparameter X and the vehicle position PV. That is, the referenceinformation REF indicates the expected value Xe of the parameter X as afunction of the vehicle position PV.

An allowable range RNG is a range of the parameter X in which theautomated driving is allowed. The allowable range RNG includes at leastthe expected value Xe. A width of the allowable range RNG ispredetermined. The width of the allowable range RNG may be set based ona standard deviation (a) of the large number of parameters X acquiredwhen the automated driving condition is satisfied. A set of the expectedvalue Xe and the allowable range RNG may be registered in the referenceinformation REF.

The automated driving management system 100 acquires the first sensorperception information SEN1 acquired by the vehicle 1 (i.e., the vehiclecontrol system 10) at the determination target position PT. The firstsensor perception information SEN1 includes an actual value Xa of theparameter X acquired at the determination target position PT. Inaddition, based on the reference information REF, the automated drivingmanagement system 100 acquires the expected value Xe associated with thedetermination target position PT. Then, the automated driving managementsystem 100 determines whether or not the automated driving condition issatisfied at the determination target position PT by comparing theactual value Xa of the parameter X acquired at the determination targetposition PT with the allowable range RNG including the expected value Xeassociated with the determination target position PT. More specifically,when the actual value Xa acquired at the determination target positionPT is within the allowable range RNG, the automated driving managementsystem 100 determines that the automated driving condition is satisfiedat the determination target position PT. On the other hand, when theactual value Xa acquired at the determination target position PTdeviates from the allowable range RNG, the automated driving managementsystem 100 determines that the automated driving condition is notsatisfied at the determination target position PT.

For example, the parameter X is the number of beam reflection points onthe road surface 2 measured by the LIDAR. The expected value Xe is anexpected value of the number of beam reflection points on the roadsurface 2 when the automated driving condition is satisfied. In rainyweather, the number of beam reflection points on the road surface 2decreases conspicuously. When the number of beam reflection points onthe road surface 2 falls below the allowable range RNG including theexpected value Xe, the automated driving management system 100determines that the automated driving condition is not satisfied at thedetermination target position PT. That is, when the “appearance” of theroad surface 2 viewed from the perception sensor of the vehicle 1 isdifferent from usual, it is determined that the automated drivingcondition is not satisfied.

When the determination target position PT is the current position of thevehicle 1 and it is determined that the automated driving condition isnot satisfied at the determination target position PT, the automateddriving management system 100 decelerates or stops the vehicle 1. Forexample, the automated driving management system 100 instructs thevehicle control system 10 to decelerate or stop the vehicle 1.

1-4. Various Forms of Automated Driving Management System

The automated driving management system 100 may be included in thevehicle control system 10 of the vehicle 1 or may be provided separatelyfrom the vehicle control system 10. The automated driving managementsystem 100 may be a management server that communicates with the vehicle1 (the vehicle control system 10). The automated driving managementsystem 100 and the vehicle control system 10 may be partially common.

FIG. 4 is a conceptual diagram for explaining a management server 1000that manages the automated driving. The management server 1000 may beconfigured by a plurality of servers that execute distributedprocessing. The management server 1000 is communicably connected to alarge number of vehicles 1 that perform the automated driving. Themanagement server 1000 collects the vehicle positions PV and the firstsensor perception information SEN1 from the large number of vehicles 1.In particular, the management server 1000 collects the vehicle positionsPV and the first sensor perception information SEN1 in the case wherethe automated driving is possible from the large number of vehicles 1.Then, the management server 1000 generates and updates theabove-described reference information REF based on the informationcollected from the large number of vehicles 1.

For example, the automated driving management system 100 is included inthe management server 1000. In this case, the management server 1000communicates with the vehicle 1 being a determination target andacquires information of the determination target position PT and thefirst sensor perception information SEN1 acquired at the determinationtarget position PT. Then, the management server 1000 performs theabove-described ODD suitability determination process based on theinformation acquired from the vehicle 1 being the determination targetand the reference information REF. When it is determined that theautomated driving condition is not satisfied at the determination targetposition PT, the management server 1000 instructs the vehicle controlsystem 10 of the vehicle 1 being the determination target to decelerateor stop.

As another example, the automated driving management system 100 may beincluded in the vehicle control system 10. In this case, the vehiclecontrol system 10 communicates with the management server 1000 andacquires the reference information REF from the management server 1000.In addition, the vehicle control system 10 acquires the first sensorperception information SEN1 at the determination target position PT.Then, the vehicle control system 10 performs the above-described ODDsuitability determination process based on the first sensor perceptioninformation SEN1 and the reference information REF. When it isdetermined that the automated driving condition is not satisfied at thedetermination target position PT, the vehicle control system 10decelerates or stops the vehicle 1.

Generalization is as follows. The automated driving management system100 includes one or more processors and one or more memory devices. Theone or more processors may be included in the vehicle control system 10,may be included in the management server 1000, or may be distributed tothe vehicle control system 10 and the management server 1000. The one ormore memory devices may be included in the vehicle control system 10,may be included in the management server 1000, or may be distributed tothe vehicle control system 10 and the management server 1000. The one ormore memory devices store the reference information REF. The one or moreprocessors acquire the first sensor perception information SEN1 andperforms the ODD suitability determination process based on the firstsensor perception information SEN1 and the reference information REF.

1-4. Effects

As described above, according to the present embodiment, the referenceinformation REF indicating the correspondence relationship between theexpected sensor perception information ESEN and the vehicle position PVis prepared. The expected sensor perception information ESEN is thefirst sensor perception information SEN1 (i.e., the result of perceptionby the perception sensor 30) expected when the automated drivingcondition is satisfied. Therefore, comparing the first sensor perceptioninformation SEN1 acquired at the determination target position PT withthe expected sensor perception information ESEN associated with thedetermination target position PT makes it possible to accuratelydetermine whether or not the automated driving condition is satisfied atthe determination target position PT. For example, as compared with acase where a parameter specifically defining the weather itself such asthe rainfall amount is used, it is possible to more accurately determinewhether or not the automated driving condition is satisfied.

In addition, not only the natural environment such as the weather butalso aging or the performance deterioration of the perception sensor 30itself affects the accuracy of the automated driving control. It isnecessary to perform the ODD suitability determination process inconsideration of various factors that affect the accuracy of theautomated driving control. The expected sensor perception informationESEN according to the present embodiment is the first sensor perceptioninformation SEN1 (i.e., the result of perception by the perceptionsensor 30) expected when the automated driving condition is satisfied.Therefore, the various factors that affect the accuracy of the automateddriving control are integrally reflected in the expected sensorperception information ESEN. Using such the expected sensor perceptioninformation ESEN and the reference information REF makes it possible toperform the ODD suitability determination process easily and with highaccuracy.

Hereinafter, specific examples of the vehicle control system 10, theautomated driving management system 100, and the ODD suitabilitydetermination process according to the present embodiment will bedescribed.

2. Example of Vehicle Control System 2-1. Configuration Example

FIG. 5 is a block diagram showing a configuration example of the vehiclecontrol system 10 according to the present embodiment. The vehiclecontrol system 10 includes a vehicle state sensor 20, a perceptionsensor 30, a position sensor 40, a travel device 50, a communicationdevice 60, and a control device 70.

The vehicle state sensor 20 detects a state of the vehicle 1. Forexample, the vehicle state sensor 20 includes a speed sensor, anacceleration sensor, a yaw rate sensor, a steering angle sensor, and thelike.

The perception sensor 30 perceives (detects) a situation around thevehicle 1. The perception sensor 30 includes a LIDAR 31, a camera 32, aradar, and the like. The LIDAR 31 emits beams and detects a reflectedbeam reflected at a reflection point to measure a relative position ofthe reflection point. The camera 32 images a situation around thevehicle 1 to acquire an image.

The position sensor 40 detects a position and an orientation of thevehicle 1. Examples of the position sensor 40 include an inertialmeasurement unit (IMU), a global navigation satellite system (GNSS)sensor, and the like.

The travel device 50 includes a steering device, a driving device, and abraking device. The steering device steers wheels. For example, thesteering device includes an electric power steering (EPS) device. Thedriving device is a power source that generates a driving force.Examples of the driving device include an engine, an electric motor, andan in-wheel motor. The braking device generates a braking force.

The communication device 60 communicates with the outside of the vehicle1. For example, the communication device 60 communicates with themanagement server 1000 (see FIG. 4 ).

The control device (controller) 70 is a computer that controls thevehicle 1. The control device 70 includes one or more processors 71(hereinafter simply referred to as a processor 71) and one or morememory devices 72 (hereinafter simply referred to as a memory device72). The processor 71 executes a variety of processing. For example, theprocessor 71 includes a central processing unit (CPU). The memory device72 stores a variety of information. Examples of the memory device 72include a volatile memory, a nonvolatile memory, a hard disk drive(HDD), a solid state drive (SSD), and the like. The control device 70may include one or more electronic control units (ECUs). A part of thecontrol device 70 may be an information processing device outside thevehicle 1. In this case, a part of the control device 70 communicateswith the vehicle 1 and remotely controls the vehicle 1.

A vehicle control program 80 is a computer program for controlling thevehicle 1. The variety of processing by the control device 70 may beimplemented by the processor 71 executing the vehicle control program80. The vehicle control program is stored in the memory device 72. Thevehicle control program 80 may be recorded on a non-transitorycomputer-readable recording medium.

2-2. Driving Environment Information

The control device 70 acquires driving environment information 200indicating a driving environment for the vehicle 1. The drivingenvironment information 200 is stored in the memory device 72. FIG. 6 isa block diagram showing an example of the driving environmentinformation 200. The driving environment information 200 includes mapinformation 210, vehicle state information 220, surrounding situationinformation 230, and vehicle position information 240.

2-2-1. Map Information

The map information 210 includes a general navigation map. The mapinformation 210 may indicate a lane configuration and a road shape. Themap information 210 may include position information of landmarks,traffic signals, signs, and so forth. The control device 70 acquires themap information 210 of a necessary area from a map database. The mapdatabase may be stored in the memory device 72 or may be managed by themanagement server 1000. In the latter case, the control device 70communicates with the management server 1000 via the communicationdevice 60 to acquire the necessary map information 210.

The map information 210 may include stationary object map information215 indicating an absolute position where a stationary object ispresent. Examples of the stationary object include a road surface 2, aroad structure 3, and the like. Examples of the road structure 3 includea wall, a guardrail, a curb, a fence, a plant, and the like.

The stationary object map information 215 may include terrain mapinformation indicating an absolute position (latitude, longitude, andaltitude) where the road surface 2 is present. The terrain mapinformation may include an evaluation value set for each absoluteposition. The evaluation value indicates “certainty (likelihood)” thatthe road surface 2 is present at the absolute position.

The stationary object map information 215 may include road structure mapinformation indicating an absolute position where the road structure 3is present. The road structure map information may include an evaluationvalue set for each absolute position. The evaluation value indicates“certainty (likelihood)” that the road structure 3 is present at theabsolute position.

2-2-2. Vehicle State Information

The vehicle state information 220 is information indicating the state ofthe vehicle 1 and includes a vehicle speed, an acceleration, a yaw rate,a steering angle, and the like. The control device 70 acquires thevehicle state information 220 from the vehicle state sensor 20. Thevehicle state information 220 may indicate a driving state (automateddriving or manual driving) of the vehicle 1.

2-2-3. Surrounding Situation Information

The surrounding situation information 230 is information indicating thesituation around the vehicle 1. The control device 70 perceives(recognizes) the situation around the vehicle 1 by using the perceptionsensor 30 to acquire the surrounding situation information 230.

For example, the surrounding situation information 230 includes pointcloud information 231 indicating a result of measurement by the LIDAR31. More specifically, the point cloud information 231 indicates arelative position (an azimuth and a distance) of each beam reflectionpoint viewed from the LIDAR 31.

The surrounding situation information 230 may include image information232 captured by the camera 32.

The surrounding situation information 230 further includes objectinformation 233 regarding an object around the vehicle 1. Examples ofthe object include a white line, the road structure 3, a surroundingvehicle 4 (e.g., a preceding vehicle, a parked vehicle, and the like), apedestrian 5, a traffic signal, a landmark, a fallen object, and thelike. The object information 233 indicates a relative position and arelative speed of the object with respect to the vehicle 1. For example,analyzing the image information 232 captured by the camera 32 makes itpossible to identify an object and calculate the relative position ofthe object. For example, the control device 70 identifies an object inthe image information 232 by using image perception AI acquired bymachine learning. It is also possible to identify an object and acquirethe relative position and the relative speed of the object based on thepoint cloud information 231 acquired by the LIDAR 31.

In the object perception, the control device 70 may utilize theabove-described stationary object map information 215. The position ofthe stationary object (e.g., the road surface 2, the road structure 3)is registered on the stationary object map information 215. Therefore,using the stationary object map information 215 makes it possible todistinguish the stationary object from other objects. More specifically,the control device 70 grasps the position of the stationary objectexisting around the vehicle 1 based on the stationary object mapinformation 215 and the vehicle position information 240. Then, thecontrol device 70 removes (thins out) the stationary object from theobjects perceived using the perception sensor 30. It is thus possible todistinguish the stationary object from the other objects (e.g., asurrounding vehicle 4, a pedestrian 5, a fallen object, and the like).For example, the control device 70 is able to detect the surroundingvehicle 4, the pedestrian 5, the fallen object, and the like on the roadsurface 2 by removing the road surface 2 indicated by the terrain mapinformation from the point cloud information 231.

2-2-4. Vehicle Position Information

The vehicle position information 240 is information indicating theposition and the orientation of the vehicle 1. The control device 70acquires the vehicle position information 240 from a result of detectionby the position sensor 40. The control device 70 may acquire highlyaccurate vehicle position information 240 by a known self-positionestimation process (localization) using the object information 233 andthe map information 210.

FIG. 7 is a conceptual diagram for explaining the self-positionestimation process (localization). Various landmarks (characteristicobjects) are present around the vehicle 1. Examples of the landmarkinclude a white line, a curb, a sign, and a pole. The control device 70uses the perception sensor 30 to perceive the landmark around thevehicle 1. The object information 233 indicates the relative position ofthe perceived landmark. Meanwhile, the absolute position of the landmarkis registered on the map information 210. The control device 70 correctsthe vehicle position information 240 such that the relative position ofthe landmark indicated by the object information 233 and the absoluteposition of the landmark acquired from the map information 210 areconsistent with each other. As a result, highly accurate vehicleposition information 240 can be acquired.

2-3. Vehicle Travel Control

The control device 70 executes vehicle travel control that controlstravel of the vehicle 1. The vehicle travel control includes steeringcontrol, acceleration control, and deceleration control. The controldevice 70 executes the vehicle travel control by controlling the traveldevice 50. More specifically, the control device 70 executes thesteering control by controlling the steering device. In addition, thecontrol device 70 executes the acceleration control by controlling thedriving device. Further, the control device 70 executes the decelerationcontrol by controlling the braking device.

2-4. Automated Driving Control

The control device 70 executes the automated driving control based onthe driving environment information 200. More specifically, the controldevice 70 generates a travel plan of the vehicle 1 based on the drivingenvironment information 200. Examples of the travel plan include keepinga current travel lane, making a lane change, making a right or leftturn, avoiding an obstacle, and the like. Further, based on the drivingenvironment information 200, the control device 70 generates a targettrajectory necessary for the vehicle 1 to travel in accordance with thetravel plan. The target trajectory includes a target position and atarget velocity. Then, the control device 70 executes the vehicle travelcontrol such that the vehicle 1 follows the target trajectory.

It should be noted that when the automated driving management system 100determines that the automated driving condition is not satisfied, thecontrol device 70 generates an emergency plan for decelerating orstopping the vehicle 1. Then, the control device 70 executes the vehicletravel control in accordance with the emergency plan to make the vehicle1 decelerate or stop.

3. Automated Driving Management System 3-1. Configuration Example

FIG. 8 is a block diagram showing a configuration example of theautomated driving management system 100 according to the presentembodiment. The automated driving management system 100 includes acommunication device 110, one or more processors 120 (hereinafter simplyreferred to as a processor 120), and one or more memory devices 130(hereinafter simply referred to as a memory device 130).

The communication device 110 communicates with the outside of theautomated driving management system 100. For example, when the automateddriving management system 100 is included in the vehicle control system10, the communication device 110 communicates with the management server1000 (see FIG. 4 ). As another example, when the automated drivingmanagement system 100 is included in the management server 1000, thecommunication device 110 communicates with the vehicle control system10.

The processor 120 executes a variety of processing. For example, theprocessor 120 includes a CPU. The memory device 130 stores a variety ofinformation. Examples of the memory device 130 include a volatilememory, a nonvolatile memory, an HDD, an SSD, and the like. When theautomated driving management system 100 is included in the vehiclecontrol system 10, the processor 120 is the same as the processor 71 ofthe vehicle control system 10, and the memory device 130 is the same asthe memory device 72 of the vehicle control system 10.

An automated driving management program 140 is a computer program formanaging the automated driving. The variety of processing by theprocessor 120 may be implemented by the processor 120 executing theautomated driving management program 140. The automated drivingmanagement program 140 is stored in the memory device 130. The automateddriving management program 140 may be recorded on a non-transitorycomputer-readable recording medium.

The reference information REF indicates a correspondence relationshipbetween the expected sensor perception information ESEN and the vehicleposition PV. The management server 1000 generates and updates thereference information REF. For example, the reference information REF isgenerated and updated based on past automated driving records of one ormore vehicles 1. As another example, the reference information REF maybe generated and updated through a simulation based on configurationinformation of an automated driving area and design information of theperception sensor 30. The reference information REF is stored in thememory device 130. When the automated driving management system 100 isincluded in the vehicle control system 10, the processor 120communicates with the management server 1000 via the communicationdevice 110 to acquire the reference information REF.

The vehicle position information 240 and the first sensor perceptioninformation SEN1 are acquired by the vehicle control system 10. Thefirst sensor perception information SEN1 indicates the result ofperception perceived by the perception sensor 30 of the vehicle 1. Forexample, the first sensor perception information SEN1 includesinformation on the stationary object (e.g., the road surface 2, the roadstructure 3) perceived by the perception sensor 30. When the automateddriving management system 100 is included in the management server 1000,the processor 120 communicates with the vehicle control system 10 viathe communication device 110 to acquire the vehicle position information240 and the first sensor perception information SEN1. The vehicleposition information 240 and the first sensor perception informationSEN1 are stored in the memory device 130.

3-2. ODD Suitability Determination Process

FIG. 9 is a flowchart showing an example of processing performed by theautomated driving management system 100 (the processor 120) according tothe present embodiment.

In Step S100, the processor 120 acquires the vehicle positioninformation 240 and the first sensor perception information SEN1. Thevehicle position information 240 includes information on thedetermination target position PT. Typically, the determination targetposition PT is a current position of the vehicle 1. As another example,the determination target position PT may be a past position of thevehicle 1. The first sensor perception information SEN1 indicates theresult of perception by the perception sensor 30 mounted on the vehicle1. For example, the first sensor perception information SEN1 includesthe actual value Xa of the parameter X perceived by the perceptionsensor 30. The processor 120 acquires the first sensor perceptioninformation SEN1 acquired at the determination target position PT.

In Step S110, the processor 120 acquires, based on the referenceinformation REF, the expected sensor perception information ESENassociated with the determination target position PT. For example, theexpected sensor perception information ESEN includes the expected valueXe of the parameter X expected when the automated driving condition issatisfied.

In Step S120, the processor 120 compares the first sensor perceptioninformation SEN1 acquired at the determination target position PT withthe expected sensor perception information ESEN associated with thedetermination target position PT.

When the first sensor perception information SEN1 acquired at thedetermination target position PT does not deviate from the expectedsensor perception information ESEN (Step S130; No), the processingproceeds to Step S140. For example, when the actual value Xa of theparameter X acquired at the determination target position PT is withinthe allowable range RNG including the expected value Xe (Step S130; No),the processing proceeds to Step S140.

In Step S140, the processor 120 determines that the automated drivingcondition is satisfied at the determination target position PT. In thiscase, the processor 120 makes the automated driving of the vehicle 1continue (Step S150).

On the other hand, when the first sensor perception information SEN1acquired at the determination target position PT deviates from theexpected sensor perception information ESEN (Step S130; Yes), theprocessing proceeds to Step S160. For example, when the actual value Xaof the parameter X acquired at the determination target position PTdeviates from the allowable range RNG including the expected value Xe(Step S130; Yes), the processing proceeds to Step S160.

In Step S160, the processor 120 determines that the automated drivingcondition is not satisfied at the determination target position PT. Inthis case, the processor 120 makes the vehicle 1 decelerate or stop(Step S170).

4. Various Examples of Odd Suitability Determination Process

Hereinafter, various examples of the ODD suitability determinationprocess according to the present embodiment will be described.

4-1. First Example

In a first example, the perception sensor 30 includes the LIDAR 31, andthe first sensor perception information SEN1 includes the point cloudinformation 231 indicating the result of measurement by the LIDAR 31.The point cloud information 231 indicates the relative position (theazimuth and the distance) of each beam reflection point viewed from theLIDAR 31.

FIG. 10 is a conceptual diagram for explaining an example of the pointcloud information 231. A first reflection point R1 is the reflectionpoint on the stationary object (e.g., the road surface 2, the roadstructure 3). A second reflection point R2 is the reflection point onthe moving object (e.g., the surrounding vehicle 4, the pedestrian 5). Anoise reflection point R3 is the reflection point caused by raindrops ordust in air.

The first reflection point R1 is detected spatially continuously over acertain range. The second reflection point R2 also is detected spatiallycontinuously over a certain range. That is, the spatially continuouspoint cloud is constituted by the first reflection point R1 or thesecond reflection point R2. Here, the above-described stationary objectmap information 215 indicates the absolute position where the stationaryobject is present. Combining the stationary object map information 215and the vehicle position information 240 makes it possible to grasp theposition at which the stationary object is assumed to be present aroundthe vehicle 1. Therefore, the processor 120 is able to classify thespatially continuous point cloud into the first reflection point R1 andthe second reflection point R2 based on the stationary object mapinformation 215 and the vehicle position information 240. In otherwords, the processor 120 is able to distinguish between the firstreflection point R1 regarding the stationary object and the secondreflection point R2 regarding the moving object. In addition, when thestationary object map information 215 includes at least one of theterrain map information and the road structure map information, it isalso possible to distinguish between the first reflection point R1regarding the road surface 2 and the first reflection point R1 regardingthe road structure 3.

On the other hand, the noise reflection points R3 are not spatiallycontinuous. Typically, each noise reflection point R3 exists alone.Therefore, the processor 120 is able to recognize the noise reflectionpoint R3 based on continuity of the point cloud. For example, assume acase where distances to a plurality of reflection points detected in acertain area are 19.8 m, 20.0 m, 5.5 m, 20.2 m, and 20.1 m. In thiscase, the reflection point whose distance is 5.5 m is the noisereflection point R3. For example, the processor 120 classifies adiscontinuous reflection point whose distance difference from areflection point for an adjacent beam is equal to or greater than apredetermined value as the noise reflection point R3.

It is assumed that the number of beams emitted from the LIDAR 31 duringone frame is “N.” A number n_t is the number of the first reflectionpoints R1 that are measured during one frame. A number n_s is the numberof the second reflection points R2 that are measured during one frame. Anumber n_n is the number of noise reflection points R3 that are measuredduring one frame. A number of no-reflection points m is the number ofbeams for which the reflected beam is not detected during one frame. Inthis case, a relationship represented by the following Equation (1) issatisfied.

N=n_t+n_s+n_n+m  Equation (1):

The first sensor perception information SEN1 includes at least one ofthe number of first reflection points n_t, the number of noisereflection points n_n, and the number of no-reflection points m. Thefirst sensor perception information SEN1 may include at least the numberof first reflection points n_t. The first sensor perception informationSEN1 may include all of the number of first reflection points n_t, thenumber of noise reflection points n_n, and the number of no-reflectionpoints m.

On the other hand, the first sensor perception information SEN1 may notinclude the number of second reflection points n_s regarding the movingobject. To generalize, second sensor perception information isinformation on the moving object perceived by the perception sensor 30.The second sensor perception information is necessary for the automateddriving control by the vehicle control system 10, but may notnecessarily be included in the first sensor perception information SEN1.

FIG. 11 is a conceptual diagram for explaining an example of thereference information REF. The reference information REF indicates acorrespondence relationship between the expected value Xe of theparameter X and the vehicle position PV. That is, the referenceinformation REF expresses the expected value Xe of the parameter X as afunction of the vehicle position PV. In the example shown in FIG. 11 ,the parameter X includes the number of first reflection points n_t, thenumber of noise reflection points n_n, and the number of no-reflectionpoints m.

As an example, the ODD suitability determination process in rainyweather will be described. A reflected beam on a diffusely-reflectingsurface is highly likely to return back to the LIDAR 31, but a reflectedbeam on a totally-reflecting surface is not likely to return back to theLIDAR 31. Therefore, in rainy weather, the first reflection point R1 onthe road surface 2 decreases. On the other hand, since raindrops in airincrease, the noise reflection point R3 increases. That is to say, inrainy weather, the number of first reflection points n_t conspicuouslydecreases, while the number of noise reflection points n_n conspicuouslyincreases. In addition, the number of no-reflection points m increasesas the number of first reflection points n_t decreases.

When the number of first reflection points n_t on the road surface 2decreases, it becomes difficult to detect a fallen object on the roadsurface 2. Further, when the number of noise reflection points n_nincreases, it becomes difficult to detect a distant object. That is, inthe rainy weather, object detection performance is deteriorated and thusthe accuracy of the automated driving control is deteriorated.Therefore, it is desirable to perform the ODD suitability determinationprocess with high accuracy.

FIG. 12 is a conceptual diagram for explaining the ODD suitabilitydetermination process using the number of first reflection points n_t. Avertical axis represents the number of first reflection points n_t, anda horizontal axis represents the vehicle position PV. The referenceinformation REF indicates a correspondence relationship between theexpected value Xe of the number of first reflection points n_t and thevehicle position PV. That is, the reference information REF expressesthe expected value Xe of the number of first reflection points n_t as afunction of the vehicle position PV.

In the example shown in FIG. 12 , a first threshold value TH1 defines alower limit value of the number of first reflection points n_t thatallows to continue the automated driving (e.g., LV4 automated driving)without deceleration. The first threshold value TH1 is set to be lowerthan the expected value Xe. A second threshold value TH2 defines a lowerlimit value of the number of first reflection points n_t that allows tocontinue the automated driving if decelerated. The second thresholdvalue TH2 is set to be further lower than the first threshold value TH1.The first threshold value TH1 and the second threshold value TH2 may beregistered on the reference information REF together with the expectedvalue Xe.

The processor 120 acquires the first sensor perception information SEN1acquired at the determination target position PT. The first sensorperception information SEN1 includes the actual value Xa of the numberof first reflection points n_t acquired at the determination targetposition PT. Based on the reference information REF, the processor 120acquires the expected value Xe associated with the determination targetposition PT. When the actual value Xa of the number of first reflectionpoints n_t is equal to or greater than the first threshold value TH1,the processor 120 determines that the automated driving condition issatisfied and the automated driving is possible. When the actual valueXa of the number of first reflection points n_t is less than the firstthreshold value TH1 and equal to or greater than the second thresholdvalue TH2, the processor 120 determines that the automated driving ispossible if decelerated. When the actual value Xa of the number of firstreflection points n_t is less than the second threshold value TH2, theprocessor 120 determines that the automated driving condition is notsatisfied and the automated driving is not possible.

FIG. 13 is a conceptual diagram for explaining the ODD suitabilitydetermination process using the number of noise reflection points n_n. Avertical axis represents the number of noise reflection points n_n, anda horizontal axis represents the vehicle position PV. The referenceinformation REF indicates a correspondence relationship between theexpected value Xe of the number of noise reflection points n_n and thevehicle position PV. That is, the reference information REF expressesthe expected value Xe of the number of noise reflection points n_n as afunction of the vehicle position PV.

In the example shown in FIG. 13 , a first threshold value TH1 defines anupper limit value of the number of noise reflection points n_n thatallows to continue the automated driving (e.g., LV4 automated driving)without deceleration. The first threshold value TH1 is set to be higherthan the expected value Xe. A second threshold value TH2 defines anupper limit value of the number of noise reflection points n_n thatallows to continue the automated driving if decelerated. The secondthreshold value TH2 is set to be further higher than the first thresholdvalue TH1. The first threshold value TH1 and the second threshold valueTH2 may be registered on the reference information REF together with theexpected value Xe.

The processor 120 acquires the first sensor perception information SEN1acquired at the determination target position PT. The first sensorperception information SEN1 includes the actual value Xa of the numberof noise reflection points n_n acquired at the determination targetposition PT. Based on the reference information REF, the processor 120acquires the expected value Xe associated with the determination targetposition PT. When the actual value Xa of the number of noise reflectionpoints n_n is equal to or less than the first threshold value TH1, theprocessor 120 determines that the automated driving condition issatisfied and the automated driving is possible. When the actual valueXa of the number of noise reflection points n_n exceeds the firstthreshold value TH1 and is equal to or less than the second thresholdvalue TH2, the processor 120 determines that the automated driving ispossible if decelerated. When the actual value Xa of the number of noisereflection points n_n exceeds the second threshold value TH2, theprocessor 120 determines that the automated driving condition is notsatisfied and the automated driving is not possible.

When the first sensor perception information SEN1 includes the number offirst reflection points n_t and the number of noise reflection pointsn_n, the processor 120 performs both the ODD suitability determinationprocess shown in FIG. 12 and the ODD suitability determination processshown in FIG. 13 . Then, the processor 120 adopts the determinationresult in which the traveling of the vehicle 1 is more restricted, thatis, the vehicle speed is lower.

4-2. Second Example

In a second example, fog is considered. In the case of fog, the numberof water droplets in the air increases greatly. Therefore, the number ofnoise reflection points n_n is greatly increased. In addition, thenumber of no-reflection points m decreases as the number of noisereflection points n_n increases. On the other hand, the number of firstreflection points n_t does not decrease so much. Therefore, using atleast one of the number of noise reflection points n_n and the number ofno-reflection points m makes it possible to appropriately perform theODD suitability determination process. The ODD suitability determinationprocess is the same as that in the first example described above.

It should be noted that a tendency of variation in the number ofreflection points is different between the case of rain and the case offog. Therefore, it is also possible to estimate a cause of the fact thatthe automated driving condition is not satisfied, based on the tendencyof variation in the number of reflection points.

4-3. Third Example

In a third example, a case where an output of the LIDAR 31 is reduced isconsidered. The reduction in the output of the LIDAR 31 occurs due toaging, failure, heat, or the like. When the output of the LIDAR 31 isreduced, the number of reflection points decreases as a whole.Especially, the number of the first reflection points R1 on a distantroad surface 2 is greatly reduced. Therefore, in the third example, thefirst reflection points R1 on the road surface 2 are further classifiedfrom a viewpoint of the distance from the vehicle 1.

For example, as shown in FIG. 14 , the road surface 2 is divided intothree types: a short-distance road surface 2 a, a medium-distance roadsurface 2 b, and a long-distance road surface 2 c. The processor 120classifies the first reflection points R1 on the road surface 2 intofirst reflection points R1 a on the road surface 2 a, first reflectionpoints R1 b on the road surface 2 b, and first reflection points R1 c onthe road surface 2 c based on the distances to the measured reflectionpoints. Numbers of first reflection points n_ta, n_tb, and n_tc are thenumbers of first reflection points R1 a, R1 b, and R1 c, respectively.

The first sensor perception information SEN1 includes the numbers offirst reflection points n_ta, n_tb, and n_tc. The reference informationREF indicates respective expected values Xe of the numbers of firstreflection points n_ta, n_tb, and n_tc. The processor 120 performs theODD suitability determination process by using the numbers of firstreflection points n_ta, n_tb, and n_tc. As a result, even when theoutput of the LIDAR 31 is reduced, the ODD suitability determinationprocess can be appropriately performed. Furthermore, it is also possibleto estimate that the cause of the fact that the automated drivingcondition is not satisfied is the reduction in the output of the LIDAR31.

4-4. Fourth Example

In a fourth example, a case where calibration of the LIDAR 31 isdeteriorated is considered. As described above, the first reflectionpoint R1 on the road structure 3 can be identified by using the roadstructure map information included in the stationary object mapinformation 215. However, when the calibration of the LIDAR 31 isdeteriorated, accuracy of the identification of the first reflectionpoint R1 using the road structure map information decreases. As aresult, the number of first reflection points R1 on the road structure 3decreases. Therefore, it is possible to appropriately perform the ODDsuitability determination process by using the number of firstreflection points n_t regarding the road structure 3. The ODDsuitability determination process is the same as that in the firstexample described above.

4-5. Fifth Example

In a fifth example, the number of landmarks perceived in thelocalization (see FIG. 7 ) is considered. Examples of the landmarkinclude a white line, a curb, a sign, a pole, and the like. The resultof perception of the landmark using the perception sensor 30 is acquiredfrom the object information 233. For example, in rainy weather, thenumber of detection of the landmarks is reduced. Especially, the whiteline, which is detected based on luminance value, becomes hard to detectwhen the road surface 2 is wet. As another example, in a case of snow,the numbers of detection of the white line and the curb aresignificantly reduced.

Therefore, in the fifth example, the ODD suitability determinationprocess is performed by using the number of landmarks perceived by theperception sensor 30. The first sensor perception information SEN1includes the number of landmarks perceived by using the perceptionsensor 30. The reference information REF indicates an expected value Xeof the number of each landmark. The processor 120 performs the ODDsuitability determination process by using the number of each landmark.

FIG. 15 is a conceptual diagram for explaining the ODD suitabilitydetermination process using the number of white lines n_wl. A verticalaxis represents the number of white lines n_wl, and a horizontal axisrepresents the vehicle position PV. The reference information REFindicates a correspondence relationship between the expected value Xe ofthe number of white lines n_wl and the vehicle position PV. That is, thereference information REF expresses the expected value Xe of the numberof white lines n_wl as a function of the vehicle position PV.

In the example shown in FIG. 15 , a first threshold value TH1 defines alower limit value of the number of white lines n_wl that allows tocontinue the automated driving (e.g., LV4 automated driving) withoutdeceleration. The first threshold TH1 is set to be lower than theexpected value Xe. A second threshold value TH2 defines a lower limitvalue of the number of white lines n_wl that allows to continue theautomated driving if decelerated. The second threshold value TH2 is setto be further lower than the first threshold value TH1. The firstthreshold value TH1 and the second threshold value TH2 may be registeredon the reference information REF together with the expected value Xe.

The processor 120 acquires the first sensor perception information SEN1acquired at the determination target position PT. The first sensorperception information SEN1 includes the actual value Xa of the numberof white lines n_wl acquired at the determination target position PT.Based on the reference information REF, the processor 120 acquires theexpected value Xe associated with the determination target position PT.When the actual value Xa of the number of white lines n_wl is equal toor greater than the first threshold value TH1, the processor 120determines that the automated driving condition is satisfied and theautomated driving is possible. When the actual value Xa of the number ofwhite lines n_wl is less than the first threshold value TH1 and equal toor greater than the second threshold value TH2, the processor 120determines that the automated driving is possible if decelerated. Whenthe actual value Xa of the white line number n_wl is less than thesecond threshold value TH2, the processor 120 determines that theautomated driving condition is not satisfied and the automated drivingis not possible.

4-6. Sixth Example

FIG. 16 is a conceptual diagram for explaining a sixth example. In thesixth example, the image (image information 232) captured by the camera32 is considered. The processor 120 can extract the road surface 2 inthe image by analyzing the image captured by the camera 32. For example,the processor 120 can extract the road surface 2 in the image byapplying semantic segmentation to the image. The segmentation (regiondivision) is a technique of grouping regions having similar featureamounts (color, texture, or the like) in the image to divide the imageinto a plurality of regions.

Quality (visibility) of the image captured by the camera greatly variesdepending on an imaging condition. For example, in rainy weather, theimage quality is deteriorated. As another example, if a lens of camera32 is dirty, the image quality is deteriorated. As still anotherexample, at night, the image quality is deteriorated due to insufficientlight intensity. When the image quality of the image is deteriorated,object detection performance based on the image is deteriorated and thusthe accuracy of the automated driving control is deteriorated.Therefore, it is desirable to perform the ODD suitability determinationprocess with high accuracy.

As described above, the processor 120 extracts the road surface 2 in theimage by analyzing the image captured by the camera 32. However, whenthe image quality of the image is deteriorated, an area of the roadsurface 2 to be extracted decreases. Therefore, in the sixth example,the ODD suitability determination process is performed by using an arearatio of the road surface 2 in the image. The first sensor perceptioninformation SEN1 includes the area ratio of the road surface 2 in theimage. The reference information REF indicates the expected value Xe ofthe area ratio of the road surface 2 in the image. The processor 120performs the ODD suitability determination process by using the arearatio of the road surface 2 in the image. The ODD suitabilitydetermination process is the same as that in the fifth example describedabove.

What is claimed is:
 1. An automated driving management system applied toa vehicle that performs automated driving by using a perception sensorfor perceiving a surrounding situation, the automated driving managementsystem comprising: one or more processors configured to acquire firstsensor perception information indicating a result of perception by theperception sensor; and one or more memory devices configured to storereference information indicating a correspondence relationship between avehicle position and expected sensor perception information that is thefirst sensor perception information expected when an automated drivingcondition is satisfied, wherein the one or more processors are furtherconfigured to: acquire, based on the reference information, the expectedsensor perception information associated with a determination targetposition; and determine whether or not the automated driving conditionis satisfied at the determination target position by comparing the firstsensor perception information acquired at the determination targetposition with the expected sensor perception information associated withthe determination target position.
 2. The automated driving managementsystem according to claim 1, wherein the first sensor perceptioninformation includes a parameter indicating the result of perception bythe perception sensor, the expected sensor perception informationincludes an expected value of the parameter expected when the automateddriving condition is satisfied, the reference information indicates acorrespondence relationship between the expected value of the parameterand the vehicle position, and the one or more processors are furtherconfigured to: acquire, based on the reference information, the expectedvalue associated with the determination target position; compare anactual value of the parameter acquired at the determination targetposition with an allowable range including the expected value associatedwith the determination target position; and when the actual value of theparameter acquired at the determination target position deviates fromthe allowable range, determine that the automated driving condition isnot satisfied at the determination target position.
 3. The automateddriving management system according to claim 1, wherein the first sensorperception information includes information on a stationary objectperceived by the perception sensor.
 4. The automated driving managementsystem according to claim 3, wherein second sensor perceptioninformation is information on a moving object perceived by theperception sensor, and the second sensor perception information is notincluded in the first sensor perception information.
 5. The automateddriving management system according to claim 1, wherein the perceptionsensor includes a laser imaging detection and ranging, and the firstsensor perception information includes a result of measurement by thelaser imaging detection and ranging.
 6. The automated driving managementsystem according to claim 5, wherein the laser imaging detection andranging emits beams and detects a reflected beam reflected at areflection point to measure a relative position of the reflection point,a number of first reflection points is a number of reflection points ona stationary object that are measured during one frame, a number ofnoise reflection points is a number of reflection points in air that aremeasured during the one frame, a number of no-reflection points is anumber of beams for which the reflected beam is not detected during theone frame, and the first sensor perception information includes at leastone of the number of first reflection points, the number of noisereflection points, and the number of no-reflection points.
 7. Theautomated driving management system according to claim 6, wherein aparameter includes the at least one of the number of first reflectionpoints, the number of noise reflection points, and the number ofno-reflection points included in the first sensor perceptioninformation, the expected sensor perception information includes anexpected value of the parameter expected when the automated drivingcondition is satisfied, the reference information indicates acorrespondence relationship between the expected value of the parameterand the vehicle position, and the one or more processors are furtherconfigured to: acquire, based on the reference information, the expectedvalue associated with the determination target position; compare anactual value of the parameter acquired at the determination targetposition with an allowable range including the expected value associatedwith the determination target position; and when the actual value of theparameter acquired at the determination target position deviates fromthe allowable range, determine that the automated driving condition isnot satisfied at the determination target position.
 8. The automateddriving management system according to claim 1, wherein the one or moreprocessors are further configured to: perceive a landmark around thevehicle by using the perception sensor; and estimate a position of thevehicle based on a result of perception of the landmark and mapinformation indicating a position of the landmark, and the first sensorperception information includes a number of landmarks perceived by usingthe perception sensor.
 9. The automated driving management systemaccording to claim 1, wherein the perception sensor includes a camerathat images a situation around the vehicle, the one or more processorsare further configured to analyze an image captured by the camera toextract a road surface in the image; and the first sensor perceptioninformation includes an area ratio of the road surface in the image. 10.An automated driving management method applied to a vehicle thatperforms automated driving by using a perception sensor for perceiving asurrounding situation, wherein first sensor perception informationindicates a result of perception by the perception sensor, and referenceinformation indicates a correspondence relationship between a vehicleposition and expected sensor perception information that is the firstsensor perception information expected when an automated drivingcondition is satisfied, the automated driving management methodcomprising: acquiring, based on the reference information, the expectedsensor perception information associated with a determination targetposition; and determining whether or not the automated driving conditionis satisfied at the determination target position by comparing the firstsensor perception information acquired at the determination targetposition with the expected sensor perception information associated withthe determination target position.